The importance of Markov decision processes, as the foundation of reinforcement learning, lies in their ability to provide a formal mathematical framework for modeling and solving sequential decision-making problems under uncertainty, enabling the development of efficient learning algorithms to optimize decision-making in dynamic environments.
